Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Data Science Interview Challenge

Challenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

Examine the distribution of a dataset.
Visualize the relationship between multiple variables.
Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

If you want to learn more about this topic or review your knowledge, we recommend taking the First Dive into seaborn Visualization and Deep Dive into the seaborn Visualization courses.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Code Description

sns.displot(x, kde=True)

This function visualizes the distribution of the dataset x. The histogram represents the data's frequency, while the KDE (Kernel Density Estimate) plot gives an idea of the data's probability density.

sns.jointplot(x=x, y=y, kind='kde')

The jointplot displays a bivariate distribution between x and y. The scatter plot represents the individual data points, while the KDE on the margins and in the center shows the density of the data.

Everything was clear?

Section 5. Chapter 1